File size: 1,190 Bytes
2359bda |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 |
"""
This basic example loads a pre-trained model from the web and uses it to
generate sentence embeddings for a given list of sentences.
"""
from sentence_transformers import SentenceTransformer, LoggingHandler
import numpy as np
import logging
#### Just some code to print debug information to stdout
np.set_printoptions(threshold=100)
logging.basicConfig(format='%(asctime)s - %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
level=logging.INFO,
handlers=[LoggingHandler()])
#### /print debug information to stdout
# Load pre-trained Sentence Transformer Model. It will be downloaded automatically
model = SentenceTransformer('all-MiniLM-L6-v2')
# Embed a list of sentences
sentences = ['This framework generates embeddings for each input sentence',
'Sentences are passed as a list of string.',
'The quick brown fox jumps over the lazy dog.']
sentence_embeddings = model.encode(sentences)
# The result is a list of sentence embeddings as numpy arrays
for sentence, embedding in zip(sentences, sentence_embeddings):
print("Sentence:", sentence)
print("Embedding:", embedding)
print("")
|